The NVIDIA RTX 3090 Ti, with its 24GB of GDDR6X VRAM, provides ample memory for running the Phi-3 Medium 14B model, especially when using quantization. The Q4_K_M quantization reduces the model's memory footprint to approximately 7GB, leaving a substantial 17GB VRAM headroom. This allows for comfortable operation without encountering out-of-memory errors and provides space for larger batch sizes or increased context length, depending on the specific inference task. The RTX 3090 Ti's 1.01 TB/s memory bandwidth ensures rapid data transfer between the GPU and memory, which is crucial for maintaining high inference speeds.
Furthermore, the RTX 3090 Ti's 10752 CUDA cores and 336 Tensor Cores are leveraged for parallel processing of the model's computations. The Tensor Cores, specifically designed for accelerating matrix multiplications inherent in deep learning, significantly boost the model's inference speed. While the TDP is high at 450W, it's a trade-off for the performance gains achieved, and proper cooling is essential to maintain optimal performance and prevent thermal throttling. The Ampere architecture provides a solid foundation for efficient execution of the Phi-3 Medium model.
Given the RTX 3090 Ti's capabilities and the quantized Phi-3 Medium model, you should experience smooth and responsive inference. Start with a batch size of 6 and a context length of 128000 tokens. Monitor GPU utilization and VRAM usage to fine-tune these parameters for optimal performance. Consider experimenting with different inference frameworks like llama.cpp or vLLM to potentially squeeze out even more performance. Ensure your system has adequate cooling to prevent thermal throttling, which can significantly impact inference speed.
If you encounter performance bottlenecks, consider further optimizing the model with techniques like dynamic quantization or pruning. However, for most use cases, the Q4_K_M quantization should provide a good balance between memory footprint and accuracy. Regularly update your NVIDIA drivers to benefit from the latest performance optimizations and bug fixes.